garage.tf.policies.categorical_gru_policy module

CategoricalGRUPolicy with model.

class CategoricalGRUPolicy(env_spec, name='CategoricalGRUPolicy', hidden_dim=32, hidden_nonlinearity=<function tanh>, hidden_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, hidden_b_init=<tensorflow.python.ops.init_ops.Zeros object>, recurrent_nonlinearity=<function sigmoid>, recurrent_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, output_nonlinearity=<function softmax>, output_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, output_b_init=<tensorflow.python.ops.init_ops.Zeros object>, hidden_state_init=<tensorflow.python.ops.init_ops.Zeros object>, hidden_state_init_trainable=False, state_include_action=True, layer_normalization=False)

Bases: garage.tf.policies.base.StochasticPolicy

A policy that contains a GRU to make prediction based on a categorical distribution.

It only works with akro.Discrete action space.

Parameters:

• env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
• name (str) – Policy name, also the variable scope.
• hidden_dim (int) – Hidden dimension for LSTM cell.
• hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation.
• hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor.
• hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.
• recurrent_nonlinearity (callable) – Activation function for recurrent layers. It should return a tf.Tensor. Set it to None to maintain a linear activation.
• recurrent_w_init (callable) – Initializer function for the weight of recurrent layer(s). The function should return a tf.Tensor.
• output_nonlinearity (callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation.
• output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor.
• output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor.
• hidden_state_init (callable) – Initializer function for the initial hidden state. The functino should return a tf.Tensor.
• hidden_state_init_trainable (bool) – Bool for whether the initial hidden state is trainable.
• state_include_action (bool) – Whether the state includes action. If True, input dimension will be (observation dimension + action dimension).
• layer_normalization (bool) – Bool for using layer normalization or not.

dist_info_sym(obs_var, state_info_vars, name=None)

Symbolic graph of the distribution.

distribution

Policy distribution.

get_action(observation)

Return a single action.

get_actions(observations)

Return multiple actions.

recurrent

Recurrent or not.

reset(dones=None)

Reset the policy.

state_info_specs

State info specification.

vectorized

Vectorized or not.